-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\knm4642\Dropbox\Nha Meo Heo Beo\_JMP\WORKING FOLDER\Table A14_Effect by patent quality.log
  log type:  text
 opened on:  18 Feb 2026, 23:53:55

. 
. //PREPARE DATA
. use "Baseline CEO firm year sample.dta", clear

. * set globals
. global firm = "firmage firmage2"

. global ceo = "gender age age2 yrinco i.education"

. global sample = "year < 2012 & !nonUS"

. global cluster = "mainethcode"

. * set sample
. qui reghdfe ash_f1allpat trust_sd ${firm} ${ceo} if ${sample}, a(boardid year) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)

. gen sample = e(sample)

. * construct additional variables
. sum f1allpatw if sample

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   f1allpatw |     29,384    13.27883    64.14359          0        634

. gen f1allpatw_sd = f1allpatw/r(mean)
(5,427 missing values generated)

. foreach var in q4 q10 q20 q40 q100 {
  2.         sum f1allpat_citff_top`var' if sample
  3.         gen f1allpat_citff_top`var'_sd = f1allpat_citff_top`var'/r(mean)
  4. }

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~q4 |     29,384    7.312143    36.52179          0        381
(11,630 missing values generated)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1a~f_topq10 |     29,384    4.072148    20.86152          0        232
(5,427 missing values generated)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~20 |     29,384    2.456065    13.09648          0        159
(11,630 missing values generated)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~40 |     29,384    1.418935    7.972571          0        108
(11,630 missing values generated)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat~100 |     29,384    .6872448    4.438824          0         74
(11,630 missing values generated)

. 
. //PREPARE TABLE
. eststo clear

. eststo col1: /// all patents
>         reghdfe f1allpatw_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =      50.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9358
                                                  Adj R-squared   =     0.9268
                                                  Within R-sq.    =     0.0056
Number of clusters (mainethcode) =         38     Root MSE        =     1.3066

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allpatw_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .1095453   .0420923     2.60   0.013     .0242582    .1948324
     firmage |   .0074783    .007837     0.95   0.346     -.008401    .0233576
    firmage2 |  -.0005411   .0002447    -2.21   0.033     -.001037   -.0000452
      gender |  -.1851806   .0649705    -2.85   0.007    -.3168233   -.0535378
         age |   .0137307   .0130185     1.05   0.298    -.0126474    .0401087
        age2 |  -.0001692   .0001227    -1.38   0.176    -.0004178    .0000795
      yrinco |   .0158896    .003682     4.32   0.000     .0084292    .0233501
             |
   education |
          2  |   .0701902   .0426612     1.65   0.108    -.0162497      .15663
          3  |   .1503157   .0730046     2.06   0.047     .0023944     .298237
          4  |   .1034619   .1026467     1.01   0.320    -.1045201    .3114439
             |
       _cons |   .2824966   .4512878     0.63   0.535    -.6318993    1.196893
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpatw if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   f1allpatw |     29,384    13.27883    64.14359          0        634

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  13.278825

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col1, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. eststo col2: /// top 25% patents
>         reghdfe f1allpat_citff_topq4_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =      70.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9325
                                                  Adj R-squared   =     0.9230
                                                  Within R-sq.    =     0.0139
Number of clusters (mainethcode) =         38     Root MSE        =     1.3858

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allpa~4_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .1279125   .0482206     2.65   0.012     .0302083    .2256167
     firmage |   .0278284   .0062088     4.48   0.000     .0152481    .0404086
    firmage2 |  -.0014404    .000238    -6.05   0.000    -.0019226   -.0009582
      gender |  -.3499711   .1776349    -1.97   0.056    -.7098936    .0099514
         age |  -.0103985   .0209174    -0.50   0.622    -.0527812    .0319843
        age2 |   .0000652   .0001861     0.35   0.728    -.0003119    .0004423
      yrinco |   .0132533   .0045258     2.93   0.006     .0040831    .0224234
             |
   education |
          2  |   .0959957   .0587922     1.63   0.111    -.0231287      .21512
          3  |   .1241494   .0905177     1.37   0.178    -.0592568    .3075556
          4  |   .1013385   .1292535     0.78   0.438    -.1605541     .363231
             |
       _cons |   1.159365   .6024444     1.92   0.062     -.061303    2.380033
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpat_citff_topq4 if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~q4 |     29,384    7.312143    36.52179          0        381

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  7.3121427

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col2, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. eststo col3: /// top 10% patents
>         reghdfe f1allpat_citff_topq10_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =     151.35
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9250
                                                  Adj R-squared   =     0.9144
                                                  Within R-sq.    =     0.0149
Number of clusters (mainethcode) =         38     Root MSE        =     1.4987

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allp~10_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |    .172183   .0512271     3.36   0.002      .068387    .2759791
     firmage |   .0302677   .0063145     4.79   0.000     .0174732    .0430621
    firmage2 |  -.0016242   .0002324    -6.99   0.000    -.0020951   -.0011533
      gender |  -.4664145   .2648376    -1.76   0.086    -1.003027    .0701975
         age |   -.014616    .027978    -0.52   0.604    -.0713048    .0420728
        age2 |     .00011   .0002436     0.45   0.654    -.0003837    .0006036
      yrinco |     .01279   .0047581     2.69   0.011     .0031492    .0224307
             |
   education |
          2  |   .0959396   .0611588     1.57   0.125      -.02798    .2198591
          3  |   .1396668   .1033536     1.35   0.185    -.0697475    .3490811
          4  |   .0949322   .1305142     0.73   0.472    -.1695148    .3593792
             |
       _cons |   1.215024   .7277419     1.67   0.103    -.2595208     2.68957
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpat_citff_topq10 if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1a~f_topq10 |     29,384    4.072148    20.86152          0        232

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  4.0721481

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col3, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. eststo col4: /// top 5% patents
>         reghdfe f1allpat_citff_topq20_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =      59.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9186
                                                  Adj R-squared   =     0.9072
                                                  Within R-sq.    =     0.0122
Number of clusters (mainethcode) =         38     Root MSE        =     1.6246

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allp~20_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .1825717   .0519967     3.51   0.001     .0772164    .2879269
     firmage |   .0256995   .0064589     3.98   0.000     .0126126    .0387865
    firmage2 |  -.0015556   .0002643    -5.89   0.000    -.0020911   -.0010201
      gender |  -.5465373   .3237948    -1.69   0.100    -1.202608    .1095333
         age |  -.0161249   .0351919    -0.46   0.649    -.0874305    .0551806
        age2 |   .0001219   .0003008     0.41   0.688    -.0004876    .0007314
      yrinco |   .0123028    .004665     2.64   0.012     .0028507    .0217549
             |
   education |
          2  |   .0876724   .0702976     1.25   0.220    -.0547642    .2301089
          3  |   .1497232   .0999479     1.50   0.143    -.0527904    .3522369
          4  |    .093389   .1286914     0.73   0.473    -.1673646    .3541425
             |
       _cons |    1.34125   .8533364     1.57   0.125    -.3877737    3.070274
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpat_citff_topq20 if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~20 |     29,384    2.456065    13.09648          0        159

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  2.4560645

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col4, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. eststo col5: /// top 2.5% patents
>         reghdfe f1allpat_citff_topq40_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =      51.74
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9084
                                                  Adj R-squared   =     0.8956
                                                  Within R-sq.    =     0.0081
Number of clusters (mainethcode) =         38     Root MSE        =     1.8157

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allp~40_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .2540166   .0637972     3.98   0.000     .1247511    .3832821
     firmage |   .0210733   .0076879     2.74   0.009     .0054961    .0366505
    firmage2 |   -.001286    .000273    -4.71   0.000    -.0018393   -.0007328
      gender |  -.6240448   .3480802    -1.79   0.081    -1.329322    .0812326
         age |  -.0122329   .0404285    -0.30   0.764    -.0941489    .0696831
        age2 |   .0000855   .0003427     0.25   0.804    -.0006089    .0007799
      yrinco |   .0121015   .0041977     2.88   0.007     .0035961    .0206068
             |
   education |
          2  |   .0930419   .0846998     1.10   0.279    -.0785761    .2646599
          3  |   .1523221   .1048975     1.45   0.155    -.0602204    .3648646
          4  |   .1675013   .1354838     1.24   0.224    -.1070149    .4420176
             |
       _cons |   .8666432   1.020467     0.85   0.401     -1.20102    2.934306
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpat_citff_topq40 if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat_~40 |     29,384    1.418935    7.972571          0        108

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  1.4189355

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col5, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. eststo col6: /// top 1% patents
>         reghdfe f1allpat_citff_topq100_sd trust_sd ${firm} ${ceo} if ${sample}, ///
>         a(boardid year) cluster(${cluster}) keepsin
WARNING: Singleton observations not dropped; statistical significance is biased (link)
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =     29,384
Absorbing 2 HDFE groups                           F(  10,     37) =      38.17
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8953
                                                  Adj R-squared   =     0.8806
                                                  Within R-sq.    =     0.0052
Number of clusters (mainethcode) =         38     Root MSE        =     2.2319

                           (Std. err. adjusted for 38 clusters in mainethcode)
------------------------------------------------------------------------------
             |               Robust
f1allp~00_sd | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
    trust_sd |   .2632696   .0784588     3.36   0.002     .1042969    .4222423
     firmage |   .0170794   .0107398     1.59   0.120    -.0046814    .0388403
    firmage2 |  -.0011605   .0003535    -3.28   0.002    -.0018767   -.0004443
      gender |   -.807704   .3884522    -2.08   0.045    -1.594783   -.0206251
         age |  -.0127431   .0464304    -0.27   0.785    -.1068201    .0813339
        age2 |   .0000719   .0004066     0.18   0.861     -.000752    .0008958
      yrinco |   .0109088   .0038739     2.82   0.008     .0030595    .0187581
             |
   education |
          2  |   .1355623   .1321001     1.03   0.311     -.132098    .4032226
          3  |   .1719455   .1285223     1.34   0.189    -.0884655    .4323564
          4  |   .2677198   .1645683     1.63   0.112    -.0657273    .6011669
             |
       _cons |   1.050235   1.005962     1.04   0.303    -.9880375    3.088507
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     boardid |      3598           0        3598     |
        year |        12           1          11     |
-----------------------------------------------------+

.                 sum f1allpat_citff_topq100 if e(sample)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
f1allpat~100 |     29,384    .6872448    4.438824          0         74

.                 estadd scalar depvarmean r(mean)

added scalar:
         e(depvarmean) =  .68724476

.                 estadd local FE         "X"

added macro:
                 e(FE) : "X"

.                 estadd local controls   "X"

added macro:
           e(controls) : "X"

.                 distinct boardid if e(sample)

         |        Observations
         |      total   distinct
---------+----------------------
 boardid |      29384       3598

.                 eststo col6, addscalars(nofirms r(ndistinct))
(e(nofirms) = 3598 added)

. 
. lab var f1allpatw_sd                "All patents"

. lab var f1allpat_citff_topq4_sd     "Top 25\%"

. lab var f1allpat_citff_topq10_sd    "Top 10\%"

. lab var f1allpat_citff_topq20_sd    "Top 5\%"

. lab var f1allpat_citff_topq40_sd    "Top 2.5\%"

. lab var f1allpat_citff_topq100_sd   "Top 1\%"

. esttab /*using "Table A14_Effect by patent quality.tex"*/, ///
>         cells(b(star fmt(3)) se(par fmt(3))) starlevels(* 0.1 ** 0.05 *** 0.01) ///
>         collabels(none) label varwidth(25) ///
>         mgroups("Normalized future top-quality patent applications", pattern(1 0 0 0 0 0) ///
>                 prefix(\multicolumn{@span}{c}{) suffix(}) span) ///
>         keep(trust_sd) coeflab(trust_sd "CEO's trust") ///
>         stats(depvarmean FE controls N nofirms, fmt(%9.3fc %9.3fc %9.0fc %9.0fc %9.0fc) ///
>                 lab("Dep. var. mean" "Firm \& Year FEs" "Baseline controls" "Observations" "Firms"))

-------------------------------------------------------------------------------------------------------------------------
                          \multicolumn{6}{c}{Normalized future top-quality patent applications}                          
                                   (1)             (2)             (3)             (4)             (5)             (6)   
                           All patents        Top 25\%        Top 10\%         Top 5\%       Top 2.5\%         Top 1\%   
-------------------------------------------------------------------------------------------------------------------------
CEO's trust                      0.110**         0.128**         0.172***        0.183***        0.254***        0.263***
                               (0.042)         (0.048)         (0.051)         (0.052)         (0.064)         (0.078)   
-------------------------------------------------------------------------------------------------------------------------
Dep. var. mean                  13.279           7.312           4.072           2.456           1.419           0.687   
Firm \& Year FEs                     X               X               X               X               X               X   
Baseline controls                    X               X               X               X               X               X   
Observations                    29,384          29,384          29,384          29,384          29,384          29,384   
Firms                            3,598           3,598           3,598           3,598           3,598           3,598   
-------------------------------------------------------------------------------------------------------------------------

. 
. cap log close
